Assessing the Quality of ChatGPT Responses to Dementia Caregivers' Questions: Qualitative Analysis.

Background: Artificial intelligence (AI) such as ChatGPT by OpenAI holds great promise to improve the quality of life of patients with dementia and their caregivers by providing high-quality responses to their questions about typical dementia behaviors. So far, however, evidence on the quality of such ChatGPT responses is limited. A few recent publications have investigated the quality of ChatGPT responses in other health conditions. Our study is the first to assess ChatGPT using real-world questions asked by dementia caregivers themselves. objectives: This pilot study examines the potential of ChatGPT-3.5 to provide high-quality information that may enhance dementia care and patient-caregiver education. Methods: Our interprofessional team used a formal rating scale (scoring range: 0-5; the higher the score, the better the quality) to evaluate ChatGPT responses to real-world questions posed by dementia caregivers. We selected 60 posts by dementia caregivers from Reddit, a popular social media platform. These posts were verified by 3 interdisciplinary dementia clinicians as representing dementia caregivers' desire for information in the areas of memory loss and confusion, aggression, and driving. Word count for posts in the memory loss and confusion category ranged from 71 to 531 (mean 218; median 188), aggression posts ranged from 58 to 602 words (mean 254; median 200), and driving posts ranged from 93 to 550 words (mean 272; median 276). Results: ChatGPT's response quality scores ranged from 3 to 5. Of the 60 responses, 26 (43%) received 5 points, 21 (35%) received 4 points, and 13 (22%) received 3 points, suggesting high quality. ChatGPT obtained consistently high scores in synthesizing information to provide follow-up recommendations (n=58, 96%), with the lowest scores in the area of comprehensiveness (n=38, 63%). Conclusions: ChatGPT provided high-quality responses to complex questions posted by dementia caregivers, but it did have limitations. ChatGPT was unable to anticipate future problems that a human professional might recognize and address in a clinical encounter. At other times, ChatGPT recommended a strategy that the caregiver had already explicitly tried. This pilot study indicates the potential of AI to provide high-quality information to enhance dementia care and patient-caregiver education in tandem with information provided by licensed health care professionals. Evaluating the quality of responses is necessary to ensure that caregivers can make informed decisions. ChatGPT has the potential to transform health care practice by shaping how caregivers receive health information.

Implications of carbon catabolite repression for Aspergillus-based cell factories: A review.

Carbon catabolite repression (CCR) is a global regulatory mechanism that allows organisms to preferentially utilize a preferred carbon source (usually glucose) by suppressing the expression of genes associated with the utilization of nonpreferred carbon sources. Aspergillus is a large genus of filamentous fungi, some species of which have been used as microbial cell factories for the production of organic acids, industrial enzymes, pharmaceuticals, and other fermented products due to their safety, substrate convenience, and well-established post-translational modifications. Many recent studies have verified that CCR-related genetic alterations can boost the yield of various carbohydrate-active enzymes (CAZymes), even under CCR conditions. Based on these findings, we emphasize that appropriate regulation of the CCR pathway, especially the expression of the key transcription factor CreA gene, has great potential for further expanding the application of Aspergillus cell factories to develop strains for industrial CAZymes production. Further, the genetically modified CCR strains (chassis hosts) can also be used for the production of other useful natural products and recombinant proteins, among others. We here review the regulatory mechanisms of CCR in Aspergillus and its direct application in enzyme production, as well as its potential application in organic acid and pharmaceutical production to illustrate the effects of CCR on Aspergillus cell factories.

A clinical study of non-bioartificial liver DPMAES support system in hepatitis B-related acute-on-chronic liver failure.

This study aims to observe the clinical efficacy of the dual plasma molecular adsorption exchange system (DPMAES) in patients with hepatitis B virus-related acute-on-chronic liver failure (HBV-ACLF), with a focus on its regulatory effect on cytokine storm. A total of 60 HBV-ACLF patients were enrolled in this study. The observation group, comprising 30 patients, received DPMAES treatment, while the control group underwent PE treatment. We compared the efficacy changes between the two groups post-treatment. A total of 55 HBV-ACLF patients who completed the study were analyzed, Patients treated with DPMAES showed significant improvements in clinical outcomes. After DPMAES treatment, HBV-ACLF patients exhibited notably 90 day survival rate increased by 18% compared to those in the PE group. Moreover, total bilirubin levels decreased markedly, albumin and platelet levels increased compared to the PE group. After DPMAES treatment, the patient showed a significant decrease in inflammatory cytokine IL-6 (t = 5.046, P < 0.001) and a significant decrease in procalcitonin (t = 4.66, P < 0.001). DPMAES was more effective than PE in rapidly reducing TBiL, improving coagulation function and mitigating cytokine storm. It maintained platelet stability more effectively while minimizing albumin consumption to a greater extent, significantly improved 90-day survival.Trial registration: Chinese Clinical Trial Registry, ChiCTR2300076117.

A single-center experience of non-bioartificial DFAPP support systems among Chinese patients with hyperlipidemic moderate/severe acute pancreatitis.

To assess the clinical efficacy of Double Filtration Plasmapheresis (DFAPP), a novel blood purification method, in treating hyperlipidemic moderate/severe pancreatitis (HL-M/SAP). A total of 68 HL-M/SAP patients were enrolled in this study. The observation group, comprising 34 patients, received DFAPP treatment, while the control group underwent CVVH + PA treatment. We compared the efficacy changes between the two groups post-treatment. Patients treated with DFAPP showed significant improvements in clinical outcomes. After 72 h of DFAPP treatment, HL-M/SAP patients exhibited notably lower multiple organ failure scores and a reduced mortality rate compared to those in the CVVH + PA group. Triglyceride levels in HL-M/SAP patients treated with DFAPP for 48 h averaged 3.75 ± 1.95, significantly lower than the 9.57 ± 3.84 levels in the CVVH + PA group (P < 0.05). Moreover, CRP levels decreased markedly, IL-17 levels diminished, IL-10 levels increased, and the decline in IL-35 levels was significantly less pronounced compared to the CVVH + PA group. The recurrence rate of pancreatitis was also significantly lower after 6 months. The early implementation of DFAPP in HL-M/SAP patients effectively reduces triglyceride levels, suppresses pro-inflammatory factors, enhances anti-inflammatory factors, and mitigates cytokine storm-induced sepsis damage. Consequently, this leads to a decrease in the incidence of multiple organ failure, improved patient survival rates, and a reduce the recurrence rate of lipogenic pancreatitis.Trial registration: Chinese Clinical Trial Registry, ChiCTR2300076066.

Variation of the Orientations of Organic Structure-Directing Agents inside the Channels of SCM-14 and SCM-15 Germanosilicates Obtained by Ab Initio Molecular Dynamic Simulations.

We report ab initio molecular dynamic simulations of the organic structure-directing agent (OSDA) in the channels of SCM-14 and SCM-15 germanosilicates for models with different germanium distribution. Since OSDA was free to move inside the channels, independent of its initial orientation after the simulations in all structures the OSDA, protonated 4-pyrrolidinopyridine, is positioned almost perpendicular to the large channels of SCM-14. The structures obtained from the dynamic simulation are more stable by 157 to 331 kJ/mol than the structures obtained by initial geometry optimization. After simulations, the average distance between the N atom of the pyridine moiety of the OSDA and O from Ge-O-Ge is shorter by 0.2 Å than the same distance obtained from initial optimization. The stretching N-H frequencies in the IR spectra of the OSDA and other calculated vibrational frequencies are not characteristic of the orientation of the molecule and cannot be used to detect it.

A three-path network with multi-scale selective feature fusion, edge-inspiring and edge-guiding for liver tumor segmentation.

Automatic liver tumor segmentation is one of the most important tasks in computer-aided diagnosis and treatment. Deep learning techniques have gained increasing popularity for medical image segmentation in recent years. However, due to the various shapes, sizes, and obscure boundaries of tumors, it is still difficult to automatically extract tumor regions from CT images. Based on the complementarity of edge detection and region segmentation, a three-path structure with multi-scale selective feature fusion (MSFF) module, multi-channel feature fusion (MFF) module, edge-inspiring (EI) module, and edge-guiding (EG) module is proposed in this paper. The MSFF module includes the process of generation, fusion, and selection of multi-scale features, which can adaptively correct the response weights in multiple branches to filter redundant information. The MFF module integrates richer hierarchical features to capture targets at different scales. The EI module aggregates high-level semantic information at different levels to obtain fine edge semantics, which is injected into the EG module for representation learning of segmentation features. Experiments on the LiTs2017 dataset show that our proposed method achieves a Dice index of 85.55% and a Jaccard index of 81.11%, which are higher than what can be obtained by the current state-of-the-art methods. Cross-dataset validation experiments conducted on 3Dircadb and Clinical datasets show the generalization and robustness of the proposed method by achieving dice indices of 80.14% and 81.68%, respectively.

Changes in the Metabolome and Nutritional Quality of Pulp from Three Types of Korla Fragrant Pears with Different Appearances as Revealed by Widely Targeted Metabolomics.

Korla fragrant pear (Pyrus sinkiangensis Yü) fruits have a unique flavor and are rich in phenolic acids, flavonoids, amino acids, and other nutrients. At present, the molecular basis of the quality differences among Korla fragrant pear fruits with a convex calyx and rough skin (RS), calyx shedding (SD), and a convex calyx (CV) remains unknown. To analyze the main metabolic components of Korla fragrant pear fruits and compare the antioxidant activities of these three fruits with different qualities, we used nutrient composition analysis and ultra-high-performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS)-based widely targeted metabolomics approaches to analyze the changes in the quality characteristics of the pulp of these three Korla fragrant pear fruits with different appearances. The nutrient composition analysis showed that the fructose and glucose contents were not significantly different, and sucrose and vitamin C contents were significantly higher in SD fruits compared with CV and RS fruits. However, the levels of flavor substances such as titratable acids, total phenols, and total flavonoids were high in the pulp of RS fruits. The metabolomics results identified 1976 metabolites that were clustered into 12 categories, and phenolic acid and flavonoid metabolites were the most abundant. The differentially accumulated metabolites (DAMs) in the fruits with different appearances were screened by multivariate statistical methods, and a total of 595 DAMs were detected. The analysis identified 300 DAMs between the CV and SD fruits, 246 DAMs between the RS and CV fruits, and 405 DAMs between the RS and SD groups. SD fruits contained the most metabolites with a high relative content, especially phenolic acids, lipids, amino acids and derivatives, alkaloids, and organic acids. Compared with CV fruits, flavonoid metabolism was more active in RS fruits, which also had a higher content of flavonoids, whereas the fewest metabolites were found in CV fruits, which also displayed less flavonoid accumulation. KEGG pathway enrichment analysis revealed that the DAMs were mainly enriched in the metabolic pathways of flavone and flavonol biosynthesis, confirming that CV fruits have decreased flavone and flavonol biosynthesis and accumulate fewer flavonoids than RS fruits, which may explain the less bitter and astringent flavor of CV fruits. However, the flavonoid content in RS fruits was very high, which may be one of the reasons why RS fruits have a harder pulp and are less juicy, more slaggy, and less flavorful. Moreover, the analysis of the antioxidant activity showed that during fruit development and maturation, RS fruits had stronger antioxidant activity than SD and CV fruits. These results provide a theoretical basis for improving the fruit quality of Korla fragrant pears and the processing of pear pulp.

Denticleless E3 ubiquitin protein ligase (DTL) maintains the proliferation and differentiation of epidermis and hair follicles during skin development.

BACKGROUND: A precise balance between the proliferation and differentiation of epidermal progenitors is required to achieve the barrier function during the development of epidermis. During the entire process of skin development, the newly formed basal layer cells divide, differentiate, and migrate outward to the surface of the skin, which is tightly regulated by a series of events related to cell cycle progression. The CRL4DTL complex (Cullin 4 RING ligase, in association with the substrate receptor DTL) has long emerged as a master regulator in various cellular processes, which mediates the degradation of key cell cycle proteins. However, the roles of DTL in regulating epidermal morphogenesis during skin development remain unclear. RESULTS: We showed that DTL deficiency in epidermal progenitor cells leads to defects in epidermal stratification and loss of hair follicles accompanied by reduced epidermal progenitor cells and disturbed cell cycle progression during skin development. Transcriptome analysis revealed that p53 pathway is activated in DTL-depleted epidermal progenitor cells. The apoptosis of epidermal cells showed in DTL deficiency mice is rescued by the absence of p53, but the proliferation and differentiation defects were p53-independent. CONCLUSION: Our findings indicate that DTL plays a vital role in epidermal malformation during skin development.

IgA nephropathy with podocytic infolding glomerulopathy.

(A) Immunofluorescence staining showed moderate immunoglobulin A depositions in the mesangial areas (++) of glomeruli (Bars = 100 µm). (B) Segmentally mild mesangial proliferation and mesangial matrix expansion (arrowhead) with mild thickening of glomerular capillary walls (PAS, ×400).

A lightweight intrusion detection method for IoT based on deep learning and dynamic quantization.

Intrusion detection ensures that IoT can protect itself against malicious intrusions in extensive and intricate network traffic data. In recent years, deep learning has been extensively and effectively employed in IoT intrusion detection. However, the limited computing power and storage space of IoT devices restrict the feasibility of deploying resource-intensive intrusion detection systems on them. This article introduces the DL-BiLSTM lightweight IoT intrusion detection model. By combining deep neural networks (DNNs) and bidirectional long short-term memory networks (BiLSTMs), the model enables nonlinear and bidirectional long-distance feature extraction of complex network information. This capability allows the system to capture complex patterns and behaviors related to cyber-attacks, thus enhancing detection performance. To address the resource constraints of IoT devices, the model utilizes the incremental principal component analysis (IPCA) algorithm for feature dimensionality reduction. Additionally, dynamic quantization is employed to trim the specified cell structure of the model, thereby reducing the computational burden on IoT devices while preserving accurate detection capability. The experimental results on the benchmark datasets CIC IDS2017, N-BaIoT, and CICIoT2023 demonstrate that DL-BiLSTM surpasses traditional deep learning models and cutting-edge detection techniques in terms of detection performance, while maintaining a lower model complexity.

Identification of two novel SALL1 mutations in chinese families with townes-brocks syndrome and literature review.

BACKGROUND: Townes-Brocks syndrome is a rare autosomal dominant genetic syndrome caused by mutations in SALL1. The clinical features of Townes-Brocks syndrome are highly heterogonous. Identification of new SALL1 mutations and study of the relation between SALL1 mutations and clinical features can facilitate diagnosis of Townes-Brocks syndrome. METHODS: We collected clinical data and blood samples of the two patients and their family members for whole-exome sequencing and Sanger sequencing. Prediction analysis of the SALL1variation protein structure was achieved using Alphafold. The clinical materials and gene sequencing results were analyzed. The clinical materials and gene sequencing results were analyzed. The related literature of Townes-Brocks syndrome were searched and the genotype-renal phenotype analysis was performed combined with this two cases. RESULTS: Based on the clinical features and gene sequencing results, the two patients were diagnosed as Townes-Brocks syndrome. Two novel SALL1 mutations (c.878-887del and c.1240G > T) were identified, both of which were pathogenic mutations. The correlation between genotypes and renal phenotypes in Townes-Brocks syndrome patients caused by SALL1 mutation were summarized. CONCLUSION: This study identified two novel mutations and provided new insights into the correlation of genotypes and renal phenotypes of Townes-Brocks syndrome.

Covalent Modification of Proteins by Osthole Reactive Metabolites using Proteomic Approaches.

BACKGROUND: Osthole (OST) is a bioactive natural coumarin derived from the plant Cnidium monnieri (L.) Cusson fruit (She Chuang Zi), which has various pharmacological and biological activities. OST contains an α,ß- unsaturated lactone, which is an electrophilic group that tends to be metabolized into reactive metabolites (RMs). Then, RMs are able to covalently modify nucleophilic amino acid (AA) residues of target proteins. However, few researchers considered the contribution of the covalent modification induced by OST or its metabolites. OBJECTIVE: This study aims to investigate the metabolic profile and the metabolites-protein modification of OST. METHODS: The metabolites of OST were qualitatively identified using UHPLC-Q-TOF-MS. The RMs modification patterns and potentially modified AA residues were confirmed by UHPLC-Q-TOF-MS using rat liver microsomes (RLMs) and model AAs. Finally, the modified peptides derived from high-abundance microsomal peptides were separated via nano-LC-Orbitrap-MS, and then RM-modified proteins were identified using a proteome discoverer. RESULTS: In the presence of RLMs, OST could rapidly be metabolized within 1 h and hardly identified at 4 h. We detected 10 OST metabolites, 13 OST metabolites-NAC (N-acetyl cysteine) adducts, 3 NAL (N-acetyl lysine) adducts, and 11 GSH (glutathione) adducts. Furthermore, 16 RM-modified protein targets were identified, many of which are included in the essential biological processes of OST's anti-Alzheimer's disease (AD) and anti-tumor. CONCLUSION: This study provides a novel perspective on the molecular mechanism of OST's pharmacological activities, as well as identifies potential targets for further development and application of OST and other Natural products (NPs).

Fibroblast growth factor 10 alleviates acute lung injury by inhibiting excessive autophagy via Nrf2.

Acute lung injury (ALI) is associated with an increased incidence of respiratory diseases, which are devastating clinical disorders with high global mortality and morbidity. Evidence confirms that fibroblast growth factors (FGFs) play key roles in mediating ALI. Mice were treated with LPS (lipopolysaccharide: 5 mg/kg, intratracheally) to establish an in vivo ALI model. Human lung epithelial BEAS-2B cells cultured in a corresponding medium with LPS were used to mimic the ALI model in vitro. In this study, we characterized FGF10 pretreatment (5 mg/kg, intratracheally) which improved LPS-induced ALI, including histopathological changes, and reduced pulmonary edema. At the cellular level, FGF10 pretreatment (10 ng/mL) alleviated LPS-induced ALI accompanied by reduced reactive oxygen species (ROS) accumulation and inflammatory responses, such as IL-1ß, IL-6, and IL-10, as well as suppressed excessive autophagy. Additionally, immunoblotting and co-immunoprecipitation showed that FGF10 activated nuclear factor erythroid-2-related factor 2 (Nrf2) signaling pathway via Nrf2 nuclear translocation by promoting the interaction between p62 and keap1, thereby preventing LPS-induced ALI. Nrf2 knockout significantly reversed these protective effects of FGF10. Together, FGF10 protects against LPS-induced ALI by restraining autophagy via p62-Kelch-like ECH-associated protein 1 (Keap1)-Nrf2 signaling pathway, implying that FGF10 could be a novel therapy for ALI.

Context fusion network with multi-scale-aware skip connection and twin-split attention for liver tumor segmentation.

Manually annotating liver tumor contours is a time-consuming and labor-intensive task for clinicians. Therefore, automated segmentation is urgently needed in clinical diagnosis. However, automatic segmentation methods face certain challenges due to heterogeneity, fuzzy boundaries, and irregularity of tumor tissue. In this paper, a novel deep learning-based approach with multi-scale-aware (MSA) module and twin-split attention (TSA) module is proposed for tumor segmentation. The MSA module can bridge the semantic gap and reduce the loss of detailed information. The TSA module can recalibrate the channel response of the feature map. Eventually, we can count tumors based on the segmentation results from a 3D perspective for cancer grading. Extensive experiments conducted on the LiTS2017 dataset show the effectiveness of the proposed method by achieving a Dice index of 85.97% and a Jaccard index of 81.56% over the state of the art. In addition, the proposed method also achieved a Dice index of 83.67% and a Jaccard index of 80.11% in 3Dircadb dataset verification, which further reflects its robustness and generalization ability.

RFPNet: Reorganizing feature pyramid networks for medical image segmentation.

Medical image segmentation is a crucial step in clinical treatment planning. However, automatic and accurate medical image segmentation remains a challenging task, owing to the difficulty in data acquisition, the heterogeneity and large variation of the lesion tissue. In order to explore image segmentation tasks in different scenarios, we propose a novel network, called Reorganization Feature Pyramid Network (RFPNet), which uses alternately cascaded Thinned Encoder-Decoder Modules (TEDMs) to construct semantic features in various scales at different levels. The proposed RFPNet is composed of base feature construction module, feature pyramid reorganization module and multi-branch feature decoder module. The first module constructs the multi-scale input features. The second module first reorganizes the multi-level features and then recalibrates the responses between integrated feature channels. The third module weights the results obtained from different decoder branches. Extensive experiments conducted on ISIC2018, LUNA2016, RIM-ONE-r1 and CHAOS datasets show that RFPNet achieves Dice scores of 90.47%, 98.31%, 96.88%, 92.05% (Average between classes) and Jaccard scores of 83.95%, 97.05%, 94.04%, 88.78% (Average between classes). In quantitative analysis, RFPNet outperforms some classical methods as well as state-of-the-art methods. Meanwhile, the visual segmentation results demonstrate that RFPNet can excellently segment target areas from clinical datasets.

A Review on the Cognitive Neural Mechanisms of Anaphor Processing During Language Comprehension.

Anaphora is an essential means of maintaining textual coherence, the phenomenon of replacing one word or phrase in the preceding part of a discourse with another. At least two crucial stages are involved in anaphor processing: bonding and resolution. The links between the anaphor and potential antecedents are established in the former stage, which would be evaluated and integrated into the latter stage. We reviewed relevant event-related potential (ERP) studies that examined the time course of anaphor processing and neural oscillation studies that explored energy changes in alpha, theta, and gamma frequency bands, which were associated with attention, working memory retrieval, and integration, respectively. The existing neuroimaging studies revealed the involvement of language processing networks and the Theory of Mind (ToM). Further research should explore the neural correlates and the effects of potential factors on anaphor processing, which could help gain a more comprehensive picture from multiple perspectives.

Thermodynamic Analysis on In Situ Underground Pyrolysis of Tar-Rich Coal: Secondary Reactions.

To develop the in situ underground pyrolysis process of tar-rich coal more scientifically, the effect of temperature and pressure on the distribution of pyrolysis products should be clarified. This paper selected the typical components in five distillates of light tar, phenol tar, naphthalene tar, washing tar, and anthracene tar as the main reaction products. 32 typical secondary reactions were constructed. Based on the thermodynamic analysis strategy, the variation of the Gibbs free energy and equilibrium constant of secondary reactions was investigated. The results showed that pressure mainly affected the reaction characteristics of molecule-increasing reactions. The Gibbs free energy value of the molecule-increasing reactions increased with increasing pressure. The trend that the reaction could proceed spontaneously gradually weakened. The initial temperature of some reactions that could proceed spontaneously would need to increase by dozens or even hundreds of degrees. Due to the influence of formation pressure, the generation of related components of light tar, naphthalene tar, washing tar, and anthracene tar would be inhibited to varying degrees in the in situ underground pyrolysis process. The secondary reactions related to phenol tar were equimolecular reactions, which were almost unaffected by stratal pressure. Axial pressure and confining pressure of different coal seam depths should be considered in the process of in situ underground pyrolysis.

Changes of maxillary central incisor and alveolar bone in Class II Division 2 nonextraction treatment with a fixed appliance or clear aligner: A pilot cone-beam computed tomography study.

INTRODUCTION: This retrospective clinical study investigated the clinical changes of maxillary central incisor and alveolar bone in Class II Division 2 nonextraction treatment with fixed appliances or clear aligners on the basis of cone-beam computed tomography. METHODS: Fifty-nine Chinese Han patients with similar demographic characteristics were collected from a conventional bracket group, a self-ligating bracket group, and a clear aligner group. All measurements about root resorption and alveolar bone thickness on the cone-beam computed tomography images were tested. Changes between pretreatment and posttreatment were evaluated by paired-sample t test. The variation among the 3 groups was compared by 1-way analysis of variance. RESULTS: The resistance center of the maxillary central incisor showed upward or forward movement, and the axial inclination was increased in 3 groups (P <0.0001). Root volume loss in the clear aligner group (23.68 ± 4.82 mm3) was significantly less than that in the fixed appliances group (28.24 ± 6.44 mm3 in the conventional bracket group, 28.17 ± 6.07 mm3 in the self-ligating bracket group) (P <0.05). All 3 groups showed a significant decrease in palatal alveolar bone and total bone thickness at all 3 levels at posttreatment. In contrast, labial bone thickness significantly increased except for crestal level l. Among the 3 groups, the clear aligner group had a prominent increase in labial bone thickness at the apical level (P = 0.0235). CONCLUSIONS: Clear aligner treatment for Class II Division 2 malocclusions could effectively reduce the incidence of fenestration and root resorption. Our findings will be beneficial to comprehensively understand the effectiveness of different appliances for Class II Division 2 malocclusions treatment.